Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The power of feedback.

This paper reviews peer-reviewed empirical studies on gamification. We create a framework for examining the effects of gamification by drawing from the definitions of gamification and the discussion on motivational affordances. The literature review covers results, independent variables (examined motivational affordances), dependent variables (examined psychological/behavioral outcomes from gamification), the contexts of gamification, and types of studies performed on the gamified systems. The paper examines the state of current research on the topic and points out gaps in existing literature. The review indicates that gamification provides positive effects, however, the effects are greatly dependent on the context in which the gamification is being implemented, as well as on the users using it. The findings of the review provide insight for further studies as well as for the design of gamified systems.

/pdf/does-gamification-work-a-literature-review-of-empirical-3d9hukqa8z.pdf

Does Gamification Work? -- A Literature Review of Empirical Studies on Gamification

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

This paper presents an in-depth analysis of a nonconventional topic-based personalization approach for adaptive educational systems (AES) that we have explored for a number of years in the context of university programming courses. With this approach both student modeling and adaptation are based on coarse-grained knowledge units that we called topics. Our motivation for the topic-based personalization was to enhance AES transparency for both teachers and students by utilizing typical topic-based course structures as the foundation for designing all aspects of an AES from the domain model to the end-user interface. We illustrate the details of the topic-based personalization technology, with the help of the Web-based educational service QuizGuide--the first system to implement it. QuizGuide applies the topic-based personalization to guide students to the right learning material in the context of an undergraduate C programming course. While having a number of architectural and practical advantages, the suggested coarse-grained personalization approach deviates from the common practices toward knowledge modeling in AES. Therefore, we believe that several aspects of QuizGuide required a detailed evaluation--from modeling accuracy to the effectiveness of adaptation. The paper discusses how this new student modeling approach can be evaluated, and presents our attempts to evaluate it from multiple different prospects. The evaluation of QuizGuide across several consecutive semesters demonstrates that, although topics do not always support precise user modeling, they can provide a basis for successful personalization in AESs.

/pdf/evaluation-of-topic-based-adaptation-and-student-modeling-in-4e8snvbpgo.pdf

Evaluation of topic-based adaptation and student modeling in QuizGuide

Closed corpus AH systems demonstrate what is possible to achieve with adaptive hypermedia technologies. However, they are impractical for dealing with the large volume of open corpus resources. Our Knowledge Sea project explores social navigation support, an approach for providing open corpus personalized guidance that is based on past learners' interaction with the system. The most recent stage of our project focuses on using annotations for social navigation support. We present here Knowledge Sea II, which implements annotation-based social navigation support, and report the results of several classroom studies, which have evaluated this technology.

/pdf/social-navigation-support-through-annotation-based-group-3zpwa4pcvb.pdf

Social navigation support through annotation-based group modeling

Knowledge Tracing is the de-facto standard for inferring student knowledge from performance data. Unfortunately, it does not allow modeling the feature-rich data that is now possible to collect in modern digital learning environments. Because of this, many ad hoc Knowledge Tracing variants have been proposed to model a specic feature of interest. For example, variants have studied the eect of students’ individual characteristics, the eect of help in a tutor, and subskills. These ad hoc models are successful for their own specic purpose, but are specied to only model a single specic feature. We present FAST (Feature Aware Student knowledge Tracing), an ecient, novel method that allows integrating general features into Knowledge Tracing. We demonstrate FAST’s exibility with three examples of feature sets that are relevant to a wide audience. We use features in FAST to model (i) multiple subskill tracing, (ii) a temporal Item Response Model implementation, and (iii) expert knowledge. We present empirical results using data collected from an Intelligent Tutoring System. We report that using features can improve up to 25% in classication performance of the task of predicting student performance. Moreover, for tting

https://educationaldatamining.org/EDM2014/uploads/procs2014/long%20papers/84_EDM-2014-Full.pdf

General Features in Knowledge Tracing: Applications to Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge

This paper explores two ways to help students locate most relevant resources in educational digital libraries. One method gives a more comprehensive access to educational resources, through multiple pathways of information access, including browsing and information visualization. The second method is to access personalized information through social navigation support. This paper presents the details of the Knowledge Sea II system for comprehensive personalized access to educational resources and also presents the results of a classroom study. The study delivered a convincing argument for the importance of providing multiple information presentations modes, showing that only about 10% of all resource accesses were made through the traditional search interface. We have also collected some solid evidence in favor of the social navigation support.

/pdf/comprehensive-personalized-information-access-in-an-xp131tv0u7.pdf

Comprehensive personalized information access in an educational digital library

Rich, interactive eLearning tools receive a lot of attention nowadays from both practitioners and researchers. However, broader dissemination of these tools is hindered by the technical difficulties of their integration into existing platforms. This article explores the technical and conceptual problems of using several interactive educational tools in the context of a single course. It presents an integrated Exploratorium for database courses, an experimental platform, which provides personalized access to several types of interactive learning activities. Several classroom studies of the Exploratorium have demonstrated its value in both the integration of several tools and the provision of personalized access.

/pdf/learning-sql-programming-with-interactive-tools-from-xtgz8dx4me.pdf

Peter Brusilovsky

Papers

Evaluation of topic-based adaptation and student modeling in QuizGuide

Social navigation support through annotation-based group modeling

General Features in Knowledge Tracing: Applications to Multiple Subskills, Temporal Item Response Theory, and Expert Knowledge

Comprehensive personalized information access in an educational digital library

Learning SQL Programming with Interactive Tools: From Integration to Personalization